Identifying optimal classification rules for geographic object-based image analysis

In supervised machine learning in the context of computer vision object recognition is achieved by training a classifier using image features of a labelled data set.. By

Coral reef mapping with object- based approach using Landsat ETM+ image on some levels of class number for coral reef benthic habitat showed that pixel-based classification

When an object is actually present in the image, and all or most of its features are found, a large number of labels are accumulated for that object at the ap- proximate position of

In supervised machine learning in the context of computer vision object recognition is achieved by training a classifier using image features of a labelled data set.. By

Similarly to the traditional hard segmentation approach, depicted to the left side, the image classification process can be understood as the sequential development of three

An alternative to pixel-based classification is object-based image analysis (OBIA), a type of image processing and classification that has provided better results when using

In this dissertation we propose and analyze several different methods for object categorization using local image features without explicitly having to establish point

The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal